Many applications for processing images seek to detect events in the video streams. A conventional technique consists in modeling from a photometric point of view the appearance of the empty scene which will be called the background model hereinafter. Any moving object appearing in the image then has inconsistencies relative to this background model and can therefore be detected.
The current techniques propose modeling of the background in the pixels or by blocks of pixels. In a first phase, on each pixel or block of pixels, a certain number of parameters are computed in order to encode the information locally. These are parameters of color, of texture or else of orientation of the gradient. During a second phase, called the phase of segmentation of the objects of interest, the current image is compared with the previously created model, the zones revealing differences being associated with probable objects of interest. For example, a fixed camera can be used to analyze road traffic. A model of the empty road and of the verges is constructed which consists in describing each pixel or block of pixels of the image. When vehicles enter the field of view of the camera, the pixels corresponding to the vehicles are different from those that have been used to generate the model. The difference between the image and the model, obtained by “background subtraction”, makes it possible to detect and segment the vehicles.
In addition to the problems of sensitivity preventing the detection of objects with photometric properties close to their environment, such as a gray vehicle on a gray road, another difficulty lies in updating the background model. Specifically, variations in the ambient luminosity create differences between the current image and the model without the objects of interest being present. The variations in the ambient luminosity are usually due to the changes in sunlight, to cloudy periods, precipitation or else to the action of the wind. These changes have to be incorporated, that is to say the model has to be updated dynamically in order to adapt it to the variations of the observed scene while maintaining the level of detection performance.
In attempts to solve this problem, several approaches propose using, in order to model the background and encode the variability of each parameter, a model called “Gaussian mixture”, this model including several Gaussian models each having a mean and a variance. These statistical systems operate quite well and support reasonable variations such as the variations in the luminosity during the day or the movement of the wind in a tree. But a difficulty lies in the speed of updating of these Gaussian models in order to follow the largest variations, the theory underlying these approaches being based on a slow and regular updating of the Gaussian model. Therefore, these systems currently do not succeed in managing the effects of events such as cloudy periods or snow falls.
In an attempt to solve this problem, F. Porikli describes, in an article entitled “Human body tracking by adaptive background models and mean-shift analysis”, a method in which the updating of a single Gaussian model, representing the background of the observed scene, is dependant on the “activity” in the image, in other words on the variability over time of the scene. In order to evaluate this “activity”, the author defines a criterion of change in luminosity in the image. Above a previously set threshold, a learning coefficient is modified proportionally to the change in luminosity. Unfortunately, this approach has two major defects. First of all, the criterion of change in luminosity is computed over the whole of the image. Therefore in the case of a cloudy period, for example, the variation in luminosity is not global but local in the scene and therefore in the image. Moreover, the very principle of threshold based on which the learning coefficient is modified poses a problem since it is determined empirically and therefore subjectively with no rule or theoretical criterion to which to be attached.